Fanny Larradet
ABSTRACT
Numerous studies on emotion recognition from physiological signals have been conducted in laboratory settings. However, differences in the data on emotions elicited in the lab and in the wild have been observed. Thus, there is a need for systems collecting and labelling emotion-related physiological data in ecological settings. This paper proposes a new solution to collect and label such data: an open-source mobile application (app) based on the appraisal theory. Our approach exploits a commercially available wearable physiological sensor connected to a smartphone. The app detects relevant events from the physiological data, and prompts the users to report their emotions using a questionnaire based on the Ortony, Clore and Collins (OCC) Model. We believe that the app can be used to collect emotional and physiological data in ecological settings and to ensure high quality of ground truth labels.
- L. Shu, J. Xie, M. Yang, Z. Li, Z. Li, D. Liao, X. Xu, X. Yang, A review of emotion recognition using physiological signals, Sensors 18 (7) (2018) 1--41.Google Scholar
Cross Ref
- F. H. Wilhelm, P. Grossman, Emotions beyond the laboratory: Theoretical fundaments, study design, and analytic strategies for advanced ambulatory assessment, Biological psychology 84 (3) (2010) 552--569.Google Scholar
- P. Schmidt, A. Reiss, R. Dürichen, K. Van Laerhoven, Labelling affective states in the wild: Practical guidelines and lessons learned, in: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, ACM, 2018, pp. 654--659. Google ScholarDigital Library
- J. Healey, L. Nachman, S. Subramanian, J. Shahabdeen, M. Morris, Out of the lab and into the fray: towards modeling emotion in everyday life, in: International Conference on Pervasive Computing, Springer, 2010, pp. 156--173. Google ScholarDigital Library
- A. Ortony, G. L. Clore, A. Collins, The cognitive structure of emotions, Cambridge university press, 1990.Google Scholar
- C. Conati, Probabilistic assessment of user's emotions in educational games, Applied artificial intelligence 16 (7--8) (2002) 555--575.Google Scholar
- C. Bartneck, Integrating the occ model of emotions in embodied characters, in: Workshop on Virtual Conversational Characters, Citeseer, 2002, pp. 39--48.Google Scholar
- M. Myrtek, G. Brügner, Perception of emotions in everyday life: studies with patients and normals, Biological psychology 42 (1--2) (1996) 147--164.Google Scholar
- F. Nasoz, K. Alvarez, C. L. Lisetti, N. Finkelstein, Emotion recognition from physiological signals using wireless sensors for presence technologies, Cognition, Technology & Work 6 (1) (2004) 4--14. Google ScholarDigital Library
- E. A. Carroll, M. Czerwinski, A. Roseway, A. Kapoor, P. Johns, K. Rowan, M. Schraefel, Food and mood: Just-in-time support for emotional eating, in: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, IEEE, 2013, pp. 252--257. Google ScholarDigital Library
- D. Beukelman, S. Fager, A. Nordness, Communication support for people with als, Neurology Research International 2011 (2011) 1--6.Google ScholarCross Ref
- M. Gjoreski, M. Luštrek, M. Gams, H. Gjoreski, Monitoring stress with a wrist device using context, Journal of biomedical informatics 73 (2017) 159--170. Google ScholarDigital Library
- K. Plarre, A. Raij, S. M. Hossain, A. A. Ali, M. Nakajima, M. Al'Absi, E. Ertin, T. Kamarck, S. Kumar, M. Scott, et al., Continuous inference of psychological stress from sensory measurements collected in the natural environment, in: Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, IEEE, 2011, pp. 97--108.Google Scholar
- K. Hovsepian, M. alAbsi, E. Ertin, T. Kamarck, M. Nakajima, S. Kumar, cstress: towards a gold standard for continuous stress assessment in the mobile environment, in: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing, ACM, 2015, pp. 493--504. Google ScholarDigital Library
- A. Zenonos, A. Khan, G. Kalogridis, S. Vatsikas, T. Lewis, M. Sooriya-bandara, Healthyoffice: Mood recognition at work using smartphones and wearable sensors, in: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), IEEE, 2016, pp. 1--6.Google ScholarCross Ref
- E. K. Gray, D. Watson, R. Payne, C. Cooper, Emotion, mood, and temperament: Similarities, differences, and a synthesis, Emotions at work: Theory, research and applications for management (2001) 21--43.Google Scholar
- K. R. Scherer, What are emotions? and how can they be measured?, Social science information 44 (4) (2005) 695--729.Google Scholar
- M. Isomursu, M. Tähti, S. Väinämö, K. Kuutti, Experimental evaluation of five methods for collecting emotions in field settings with mobile applications, International Journal of Human-Computer Studies 65 (4) (2007) 404--418. Google ScholarDigital Library
- A. Muaremi, B. Arnrich, G. Tröster, Towards measuring stress with smartphones and wearable devices during workday and sleep, Bio-NanoScience 3 (2) (2013) 172--183.Google Scholar
- R. Kocielnik, N. Sidorova, F. M. Maggi, M. Ouwerkerk, J. H. Westerink, Smart technologies for long-term stress monitoring at work, in: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, IEEE, 2013, pp. 53--58.Google ScholarCross Ref
- A. Meschtscherjakov, A. Weiss, T. Scherndl, Utilizing emoticons on mobile devices within esm studies to measure emotions in the field, Proc. MME in conjunction with MobileHCI 9 (2009) 3361--3366.Google Scholar
- C. Dobbins, S. Fairclough, A mobile lifelogging platform to measure anxiety and anger during real-life driving, in: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2017, pp. 327--332.Google ScholarCross Ref
- G. N. Dikecligil, L. R. Mujica-Parodi, Ambulatory and challenge-associated heart rate variability measures predict cardiac responses to real-world acute emotional stress, Biological psychiatry 67 (12) (2010) 1185--1190.Google Scholar
- Empatica, empatica, www.empatica.com (accessed 9 july 2019).Google Scholar
- M. Mortillaro, B. Meuleman, K. R. Scherer, Advocating a componential appraisal model to guide emotion recognition, International Journal of Synthetic Emotions (IJSE) 3 (1) (2012) 18--32. Google ScholarDigital Library
- C. Smith, Dimensions of appraisal and physiological response in emotion, Journal of Personality and Social Psychology (1989) 339--353.Google ScholarCross Ref
- G. L. Clore, A. Ortony, Psychological construction in the occ model of emotion, Emotion Review 5 (4) (2013) 335--343.Google ScholarCross Ref
- I. B. Mauss, M. D. Robinson, Measures of emotion: A review, Cognition and emotion 23 (2) (2009) 209--237.Google Scholar
- G. E. Schwartz, D. A. Weinberger, J. A. Singer, Cardiovascular differentiation of happiness, sadness, anger, and fear following imagery and exercise., Psychosomatic medicine 43 (4) (1981) 343--364.Google Scholar
- F. Larradet, Mafed, https://gitlab.com/flarradet/mafed (accessed 9 july 2019).Google Scholar
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